I was creating a regression model using simple neural network and built my model as follows:

The shape of input variable X_train is (1086, 76), which is a DataFrame

```
from torch import nn
from torch.nn import functional as F
class NeuralNet(nn.Module):
def __init__(self):
super(NeuralNet, self).__init__()
self.linear1 = nn.Linear(76, 30)
self.linear2 = nn.Linear(30, 10);
self.linear3 = nn.Linear(10, 1)
# self.relu = nn.ReLU()
def forward(self, x):
x = F.relu(self.linear1(x))
x = F.relu(self.linear2(x))
x = F.relu(self.linear3(x))
return x
model = NeuralNet()
print(model)
```

Here I have used a DataLoader, but when I try to run it without the DatLoader, it gives me the same error with a DataFrame. I started writing the training code:

```
import torch.optim as optim
import torch
from torch.utils.data import DataLoader
train_loader = DataLoader(X_train)
epochs = 10
for i,e in enumerate(range(epochs)):
optimizer.zero_grad() # Reset the grads
output = model.forward(train_loader) # Forward pass
loss = criterion(output.view(output.shape[0]),y) # Calculate loss
print(f"Epoch - {i+1}, Loss - {round(loss.item(),3)}") # Print loss
loss.backward() # Backpropagation
optimizer.step() # Optimizer one step
```

Where it has been giving me an error:

```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-165-69744667c9a4> in <module>
8 for i,e in enumerate(range(epochs)):
9 optimizer.zero_grad() # Reset the grads
---> 10 output = model.forward(train_loader) # Forward pass
11 loss = criterion(output.view(output.shape[0]),y) # Calculate loss
12 print(f"Epoch - {i+1}, Loss - {round(loss.item(),3)}") # Print loss
<ipython-input-164-caccab51ac99> in forward(self, x)
9 # self.relu = nn.ReLU()
10 def forward(self, x):
---> 11 x = F.relu(self.linear1(x))
12 x = F.relu(self.linear2(x))
13 x = F.relu(self.linear3(x))
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
548 result = self._slow_forward(*input, **kwargs)
549 else:
--> 550 result = self.forward(*input, **kwargs)
551 for hook in self._forward_hooks.values():
552 hook_result = hook(self, input, result)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input)
85
86 def forward(self, input):
---> 87 return F.linear(input, self.weight, self.bias)
88
89 def extra_repr(self):
/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1606 if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(tens_ops):
1607 return handle_torch_function(linear, tens_ops, input, weight, bias=bias)
-> 1608 if input.dim() == 2 and bias is not None:
1609 # fused op is marginally faster
1610 ret = torch.addmm(bias, input, weight.t())
AttributeError: 'DataLoader' object has no attribute 'dim'
```

Can some one help me resolving it ?